Add pipeline tag

This PR adds the metadata to the model.

README.md

@@ -1,166 +1,167 @@
----
-license: apache-2.0
----
-# Implementation of EasyControl
-
-EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer
-
-<a href='https://arxiv.org/pdf/2503.07027'><img src='https://img.shields.io/badge/Technique-Report-red'></a> 
-<a href="https://github.com/Xiaojiu-z/EasyControl/tree/dev"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub"></a>
-
-> *[Yuxuan Zhang](https://xiaojiu-z.github.io/YuxuanZhang.github.io/), [Yirui Yuan](https://github.com/Reynoldyy), [Yiren Song](https://scholar.google.com.hk/citations?user=L2YS0jgAAAAJ), [Haofan Wang](https://haofanwang.github.io/), [Jiaming Liu](https://scholar.google.com/citations?user=SmL7oMQAAAAJ&hl=en)*
-> <br>
-> Tiamat AI, ShanghaiTech University, National University of Singapore, Liblib AI
-
-<img src='assets/teaser.jpg'>
-
-## Features
-* **Motivation:** The architecture of diffusion models is transitioning from Unet-based to DiT (Diffusion Transformer). However, the DiT ecosystem lacks mature plugin support and faces challenges such as efficiency bottlenecks, conflicts in multi-condition coordination, and insufficient model adaptability, particularly in zero-shot multi-condition combination scenarios where these issues are most pronounced.
-* **Contribution:** We propose EasyControl, an efficient and flexible unified conditional DiT framework. By incorporating a lightweight Condition Injection LoRA module, a Position-Aware Training Paradigm, and a combination of Causal Attention mechanisms with KV Cache technology, we significantly enhance model compatibility, generation flexibility, and inference efficiency.
-<img src='assets/method.jpg'>
-
-## Download
-
-You can download the model directly from [Hugging Face](https://huggingface.co/EasyControl/EasyControl).
-Or download using Python script:
-
-```python
-from huggingface_hub import hf_hub_download
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/canny.safetensors", local_dir="./models")
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/depth.safetensors", local_dir="./models")
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/hedsketch.safetensors", local_dir="./models")
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/inpainting.safetensors", local_dir="./models")
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/pose.safetensors", local_dir="./models")
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/seg.safetensors", local_dir="./models")
-hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/subject.safetensors", local_dir="./models")
-```
-
-If you cannot access Hugging Face, you can use [hf-mirror](https://hf-mirror.com/) to download the models:
-```python
-export HF_ENDPOINT=https://hf-mirror.com
-huggingface-cli download --resume-download Xiaojiu-Z/EasyControl --local-dir checkpoints --local-dir-use-symlinks False
-```
-
-## Usage
-Here's a basic example of using EasyControl. For more details, please follow the instructions in our [__GitHub repository__](https://github.com/Xiaojiu-z/EasyControl):
-
-### Model Initialization
-
-```python
-import torch
-from PIL import Image
-from src.pipeline import FluxPipeline
-from src.transformer_flux import FluxTransformer2DModel
-from src.lora_helper import set_single_lora, set_multi_lora
-
-def clear_cache(transformer):
-    for name, attn_processor in transformer.attn_processors.items():
-        attn_processor.bank_kv.clear()
-
-# Initialize model
-device = "cuda"
-base_path = "FLUX.1-dev"  # Path to your base model
-pipe = FluxPipeline.from_pretrained(base_path, torch_dtype=torch.bfloat16, device=device)
-transformer = FluxTransformer2DModel.from_pretrained(
-    base_path, 
-    subfolder="transformer",
-    torch_dtype=torch.bfloat16, 
-    device=device
-)
-pipe.transformer = transformer
-pipe.to(device)
-
-# Load control models
-lora_path = "./models"
-control_models = {
-    "canny": f"{lora_path}/canny.safetensors",
-    "depth": f"{lora_path}/depth.safetensors",
-    "hedsketch": f"{lora_path}/hedsketch.safetensors",
-    "pose": f"{lora_path}/pose.safetensors",
-    "seg": f"{lora_path}/seg.safetensors",
-    "inpainting": f"{lora_path}/inpainting.safetensors",
-    "subject": f"{lora_path}/subject.safetensors",
-}
-```
-
-### Single Condition Control
-
-```python
-# Single spatial condition control example
-path = control_models["canny"]
-set_single_lora(pipe.transformer, path, lora_weights=[1], cond_size=512)
-
-# Generate image
-prompt = "A nice car on the beach"
-spatial_image = "./test_imgs/canny.png"
-
-image = pipe(
-    prompt,
-    height=720,
-    width=992,
-    guidance_scale=3.5,
-    num_inference_steps=25,
-    max_sequence_length=512,
-    generator=torch.Generator("cpu").manual_seed(5),
-    spatial_images=[spatial_image],
-    cond_size=512,
-).images[0]
-
-# Clear cache after generation
-clear_cache(pipe.transformer)
-```
-
-### Multi-Condition Control
-
-```python
-# Multi-condition control example
-paths = [control_models["subject"], control_models["inpainting"]]
-set_multi_lora(pipe.transformer, paths, lora_weights=[[1], [1]], cond_size=512)
-
-prompt = "A SKS on the car"
-subject_images = ["./test_imgs/subject_1.png"]
-spatial_images = ["./test_imgs/inpainting.png"]
-
-image = pipe(
-    prompt,
-    height=1024,
-    width=1024,
-    guidance_scale=3.5,
-    num_inference_steps=25,
-    max_sequence_length=512,
-    generator=torch.Generator("cpu").manual_seed(42),
-    subject_images=subject_images,
-    spatial_images=spatial_images,
-    cond_size=512,
-).images[0]
-
-# Clear cache after generation
-clear_cache(pipe.transformer)
-```
-
-
-## Usage Tips
-
-- Clear cache after each generation using `clear_cache(pipe.transformer)`
-- For optimal performance:
-  - Start with `guidance_scale=3.5` and adjust based on results
-  - Use `num_inference_steps=25` for a good balance of quality and speed
-- When using set_multi_lora api, make sure the subject lora path(subject) is before the spatial lora path(canny, depth, hedsketch, etc.).
-
-
-## Disclaimer
-The code of EasyControl is released under [Apache License](https://github.com/Xiaojiu-Z/EasyControl?tab=Apache-2.0-1-ov-file#readme) for both academic and commercial usage. Our released checkpoints are for research purposes only. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users.
-
-
-## Citation
-```
-@misc{zhang2025easycontroladdingefficientflexible,
-      title={EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer}, 
-      author={Yuxuan Zhang and Yirui Yuan and Yiren Song and Haofan Wang and Jiaming Liu},
-      year={2025},
-      eprint={2503.07027},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2503.07027}, 
-}
-```
+---
+license: apache-2.0
+pipeline_tag: text-to-image
+---
+# Implementation of EasyControl
+
+EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer
+
+<a href='https://arxiv.org/pdf/2503.07027'><img src='https://img.shields.io/badge/Technique-Report-red'></a> 
+<a href="https://github.com/Xiaojiu-z/EasyControl/tree/dev"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub"></a>
+
+> *[Yuxuan Zhang](https://xiaojiu-z.github.io/YuxuanZhang.github.io/), [Yirui Yuan](https://github.com/Reynoldyy), [Yiren Song](https://scholar.google.com.hk/citations?user=L2YS0jgAAAAJ), [Haofan Wang](https://haofanwang.github.io/), [Jiaming Liu](https://scholar.google.com/citations?user=SmL7oMQAAAAJ&hl=en)*
+> <br>
+> Tiamat AI, ShanghaiTech University, National University of Singapore, Liblib AI
+
+<img src='assets/teaser.jpg'>
+
+## Features
+* **Motivation:** The architecture of diffusion models is transitioning from Unet-based to DiT (Diffusion Transformer). However, the DiT ecosystem lacks mature plugin support and faces challenges such as efficiency bottlenecks, conflicts in multi-condition coordination, and insufficient model adaptability, particularly in zero-shot multi-condition combination scenarios where these issues are most pronounced.
+* **Contribution:** We propose EasyControl, an efficient and flexible unified conditional DiT framework. By incorporating a lightweight Condition Injection LoRA module, a Position-Aware Training Paradigm, and a combination of Causal Attention mechanisms with KV Cache technology, we significantly enhance model compatibility, generation flexibility, and inference efficiency.
+<img src='assets/method.jpg'>
+
+## Download
+
+You can download the model directly from [Hugging Face](https://huggingface.co/EasyControl/EasyControl).
+Or download using Python script:
+
+```python
+from huggingface_hub import hf_hub_download
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/canny.safetensors", local_dir="./models")
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/depth.safetensors", local_dir="./models")
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/hedsketch.safetensors", local_dir="./models")
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/inpainting.safetensors", local_dir="./models")
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/pose.safetensors", local_dir="./models")
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/seg.safetensors", local_dir="./models")
+hf_hub_download(repo_id="Xiaojiu-Z/EasyControl", filename="models/subject.safetensors", local_dir="./models")
+```
+
+If you cannot access Hugging Face, you can use [hf-mirror](https://hf-mirror.com/) to download the models:
+```python
+export HF_ENDPOINT=https://hf-mirror.com
+huggingface-cli download --resume-download Xiaojiu-Z/EasyControl --local-dir checkpoints --local-dir-use-symlinks False
+```
+
+## Usage
+Here's a basic example of using EasyControl. For more details, please follow the instructions in our [__GitHub repository__](https://github.com/Xiaojiu-z/EasyControl):
+
+### Model Initialization
+
+```python
+import torch
+from PIL import Image
+from src.pipeline import FluxPipeline
+from src.transformer_flux import FluxTransformer2DModel
+from src.lora_helper import set_single_lora, set_multi_lora
+
+def clear_cache(transformer):
+    for name, attn_processor in transformer.attn_processors.items():
+        attn_processor.bank_kv.clear()
+
+# Initialize model
+device = "cuda"
+base_path = "FLUX.1-dev"  # Path to your base model
+pipe = FluxPipeline.from_pretrained(base_path, torch_dtype=torch.bfloat16, device=device)
+transformer = FluxTransformer2DModel.from_pretrained(
+    base_path, 
+    subfolder="transformer",
+    torch_dtype=torch.bfloat16, 
+    device=device
+)
+pipe.transformer = transformer
+pipe.to(device)
+
+# Load control models
+lora_path = "./models"
+control_models = {
+    "canny": f"{lora_path}/canny.safetensors",
+    "depth": f"{lora_path}/depth.safetensors",
+    "hedsketch": f"{lora_path}/hedsketch.safetensors",
+    "pose": f"{lora_path}/pose.safetensors",
+    "seg": f"{lora_path}/seg.safetensors",
+    "inpainting": f"{lora_path}/inpainting.safetensors",
+    "subject": f"{lora_path}/subject.safetensors",
+}
+```
+
+### Single Condition Control
+
+```python
+# Single spatial condition control example
+path = control_models["canny"]
+set_single_lora(pipe.transformer, path, lora_weights=[1], cond_size=512)
+
+# Generate image
+prompt = "A nice car on the beach"
+spatial_image = "./test_imgs/canny.png"
+
+image = pipe(
+    prompt,
+    height=720,
+    width=992,
+    guidance_scale=3.5,
+    num_inference_steps=25,
+    max_sequence_length=512,
+    generator=torch.Generator("cpu").manual_seed(5),
+    spatial_images=[spatial_image],
+    cond_size=512,
+).images[0]
+
+# Clear cache after generation
+clear_cache(pipe.transformer)
+```
+
+### Multi-Condition Control
+
+```python
+# Multi-condition control example
+paths = [control_models["subject"], control_models["inpainting"]]
+set_multi_lora(pipe.transformer, paths, lora_weights=[[1], [1]], cond_size=512)
+
+prompt = "A SKS on the car"
+subject_images = ["./test_imgs/subject_1.png"]
+spatial_images = ["./test_imgs/inpainting.png"]
+
+image = pipe(
+    prompt,
+    height=1024,
+    width=1024,
+    guidance_scale=3.5,
+    num_inference_steps=25,
+    max_sequence_length=512,
+    generator=torch.Generator("cpu").manual_seed(42),
+    subject_images=subject_images,
+    spatial_images=spatial_images,
+    cond_size=512,
+).images[0]
+
+# Clear cache after generation
+clear_cache(pipe.transformer)
+```
+
+
+## Usage Tips
+
+- Clear cache after each generation using `clear_cache(pipe.transformer)`
+- For optimal performance:
+  - Start with `guidance_scale=3.5` and adjust based on results
+  - Use `num_inference_steps=25` for a good balance of quality and speed
+- When using set_multi_lora api, make sure the subject lora path(subject) is before the spatial lora path(canny, depth, hedsketch, etc.).
+
+
+## Disclaimer
+The code of EasyControl is released under [Apache License](https://github.com/Xiaojiu-Z/EasyControl?tab=Apache-2.0-1-ov-file#readme) for both academic and commercial usage. Our released checkpoints are for research purposes only. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users.
+
+
+## Citation
+```
+@misc{zhang2025easycontroladdingefficientflexible,
+      title={EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer}, 
+      author={Yuxuan Zhang and Yirui Yuan and Yiren Song and Haofan Wang and Jiaming Liu},
+      year={2025},
+      eprint={2503.07027},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2503.07027}, 
+}
+```